It’s fair to say that big data has transformed the automotive industry in recent times. The Internet of Things (IoT) extended to cars a while ago, beginning the evolution of the automobile from manpowered vehicle capable simply of getting us from A to B, to a connected, intelligent device powered by and able to generate vast amounts of data.
Linking a car to wider information systems creates enormous scope for data-led innovation in many areas, from navigation to entertainment, traffic management to product design. Each of these systems then generates data of its own from every individual connected car; data that can be used to shed light on driver habits and vehicle usage, as well as to inform future applications. Recent research suggests that the global automotive industry will have amassed more than $3.3 billion in big data investments by the end of 2018.
Black boxes insurance has to be one of the most impactful and high-profile data innovations to hit the automotive sector over the last few years. We’ve taken a closer look at the data that powers them.
Black boxes – or telematics boxes to use their proper name – have been an established part of the motor insurance industry for many years now. There were estimated to be around 750,000 cars in Britain fitted with a black box at the end of 2017, figures which have no doubt spiked further since.
But what exactly are telematics boxes, and what do they do? Simply put, a black box is installed behind the dashboard of a car in order to measure and record a number of different metrics as you drive. Data the box records is available online to the car’s registered owner, so that they can assess their own driving style, but in most cases, a black box’s main function is to feed information back to an insurance company. Armed with a personalised view of a customer’s driving habits, a car insurance provider can create an accurate and completely bespoke premium based upon it.
Here’s a more detailed picture of the type of data a black box records, which is used by an insurance company to determine a driver score:
The more time you spend behind the wheel, the higher the probability that you’ll be involved in an accident, and vice versa. Statistics also show that longer journeys taken less often are less risky than a higher number of shorter trips.
The time of day you tend to use your car could also have an impact on your personalised premium. Rush hour driving tends to carry a higher likelihood of knocks and bumps, while a lot of night time motorway driving could also be considered high risk.
The location of your car when it’s not in use has always had an effect on how much you pay for insurance, but now a black box can record exactly where it is overnight.
Black boxes can tell what kind of road you are driving on, so they can also tell when you’re exceeding the speed limit. Frequently breaking the limit can lead to intervention from your insurance company too – they might get it touch advising you to slow down. In extreme cases, high speeds can even result in a cancellation of your policy.
An abrupt or overly heavy braking style recorded by a black box can be seen as an example of unsafe driving. Conversely, leaving plenty of time to brake before junctions can help keep your premium down.
Taking long journeys every now again may be preferable to lots of short trips, but a black box will also take rest periods, or a lack of them, into account. Safe drivers need to take regular breaks when covering long distances – of at least 15 minutes during a three-hour journey, according to the AA.
These are just some of the detailed metrics a black box tots up when in place on a vehicle, which suggests exactly how in depth a profile insurance companies can create of each customer on an individual basis. It’s not difficult to see how good driving can quickly pay off in terms of a lower premium and, as telematics uptake increases further, black boxes seem likely to become a classic example of a data value exchange many consumers will be only too happy to enter into.
Other types of insurers utilise data insights to interpret relevant costs in much the same way, creating premiums through data provided by the customer. Complex data models and algorithms enable insurers to determine individual premiums, allowing for outside factors derived from historical data and trends.
When it comes to life insurance, a smoker may be given a higher premium than a non-smoker, because historical data indicates the likelihood of a pay out being higher. Both the automotive and insurance industries can ensure accurate and personalised relationships with consumers through the use of big data.
Similarly, data can be used to create a better value marketing proposition for consumers with the right know-how. Using data insight to understand more about what consumers want and need should be key to any marketing plan, and personalising services and outputs that enhance their experience of your brand can help to turn them into loyal customers that return time and time again.
Using big data to forecast trends, personalise communications and identify propensities is just one of the ways that the Quant team ensure CRM success. If we can help you reveal business-boosting data insight for your organisation, please don’t hesitate to contact us.